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Kimball and Inmon
How anyone define the pracical difference between Kimball & Inmon?
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shashk- Member Since Jul-2010 | Aug 25th, 2010
Bill Inmon's paradigm: Data warehouse is one part of the overall business intelligence system. An enterprise has one data warehouse, and data marts source their information from the data warehouse. In the data warehouse, information is stored in 3rd normal form.
Ralph Kimball's paradigm: Data warehouse is the conglomerate of all data marts within the enterprise. Information is always stored in the dimensional model.There is no right or wrong between these two ideas, as they represent different data warehousing philosophies.
In reality, the data warehouse in most enterprises are closer to Ralph Kimball's idea. This is because most data warehouses started out as a departmental effort, and hence they originated as a data mart. Only when more data marts are built later do they evolve into a data warehouse.shashk
- Aug 25th, 2010
Bill Inmon's paradigm: Data warehouse is one part of the overall business intelligence system. An enterprise has one data warehouse, and data marts source their information from the data warehouse...
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Degenarated Dimension
What is the use of Degenerated dimension? Where it is used?
gargupendar
- Mar 24th, 2011
A degenerate dimension is a dimension which is derived from the fact table and doesn't have its own dimension table.Example : Receipt number is the perfect example for degenerate dimension.
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What is dimension modeling?
joanitah
- Mar 12th, 2011
This is a process and outcomes of modeling a logical database schemas created to support OLAP and data warehousing solutions.
Hakoonamatata
- Jul 9th, 2007
Here's a different way to look at dimensional modelling:Consider data to be a flat table for now. It would have some columns that contain numbers that need to be used in calcualtions later (e.g. b...
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Tracing Options available in Data Warehousing
What are the different tracing options available? Can tracing options be set for individual transformations?
joshmohit
- Nov 3rd, 2010
I am not sure if this pertains to the datawarehouse in general but we do have concept of audit dimensions. Also, dimensions with effective dating can help tracing the changes.
sraghav20
- Oct 7th, 2010
I guess, this question should be directed towards particular tools... as a concept, data warehousing would be hard pressed to have any logging...
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Importance of SCD
How is SCD helpful in real time? Give example by taking banking project.
rummyraj
- Oct 13th, 2010
Customer dimension is a good example. Customer address and Phone number frequently changes. So you will update the customer dimension for these changes.Hence its called as SCD. There are several types though. This example is type1 SCD.
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Dynamic Insert
What is Dynamic Insert?
mkm187
- Oct 5th, 2010
INSERT FIRSTWHEN SAL > 25000 THENINTO special_sal VALUES(DEPTID, SAL)WHEN HIREDATE like ('%00%') THENINTO hiredate_history_00 VALUES(DEPTID,HIREDATE)WHEN HIREDATE like ('%99%') THEN...
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What is the main difference between star and snowflake star schema? Which one is better and why?
bcsandip
- May 25th, 2010
Star Schema: 1. Dimension tables directly connected to Fact Table2. Contain de-normalized data3. Data modifications are very easily4. Highest flexibility5. Best performance6. Takes more memory space t...
geetu rani
- Mar 27th, 2008
STAR SCHEMA HAS fact table linked with dimension table directlywhile in snow flake schema the dimension tables can have futher hierarchies between them .it means in snow flake schema the tables are no...
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AutoSys Jobs Schedule
How can we schedule our jobs in third party tool - AutoSys?
saigarneni
- May 18th, 2010
We need to write shell script to run the job and write JIL on autosys to schedule the job
varghesebinu
- Dec 10th, 2009
Autosys is a total job scheduling solution that allows you to define the event dependencies, time schedules alerts etc making it a complete data center automation tool. An AutoSys job is any single co...
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Merge Dimension
What is the merge dimension?
Nicolas
- Apr 17th, 2010
Merge dimension is the action in Web Intelligence XI that allows to synchronize two or more data providers through common dimension. Once created, the merged dimension can be used as an object for rep...
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What is the difference between database and data warehousing?What is the main advantages of data warehousingWhat is mean by data stageWhat is the famous tools used for data warehousing?What is the architecture of data warehouisng model?
brahmiii
- Feb 28th, 2010
Database is the root of any data releted operations where as datawarehousing is a schema or process how effectively we access or utilize a database. Datawarehousing follows the star schema and snowflake schema as its basic schemas and we two other schemas also which are not much important.
sap_23
- Sep 3rd, 2009
Database: A database is used to record, retrieve, update business transactions which are occurred daily in business life. Data ware House: A data warehouse is used to record historical data, purely for analysis purpose for top management.
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Staging and Operational Data Store
What are Staging and Operational Data Stores and what is their difference?
Why does one need staging and why does one need ODS? What will happen if they are not there? Who and how is it decided that a staging or ODS is needed? Which comes first staging or ODS?jryan999
- Feb 3rd, 2010
An Operational Data Store (ODS) is a repository of active "operational" data. It's typically used to hold data for a short period (eg. Days or weeks at most), to help the business ...
srilu1234
- Jan 19th, 2010
Stagining and operational data store are different databases.It can be one database aswell to reduce the cost to maitain two databases.Staging is the place where cleansing is done and Operational Stor...
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ETL testing
How can we do the ETL testing on Data WareHouse?
anita_09
- Sep 10th, 2009
ETL testing validates that data is transformed correctly from OLTP to data warehouse. Validating the data transformed includes following main verification points: Ensures that all expected...
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What is data cleaning? How can we do that?
sub_guha
- Jan 25th, 2010
Data cleaning, technically called "Data Cleansing"is a group of methods for making data more reliable and accurate. Usually companies store data in warehouses so they can make meaning out of...
Hakoonamatata
- Jul 9th, 2007
Data cleaning is a self explainatory term. Most of the data warehouses in the world source data from multiple systems - systems that were created long before data warehousing was well understood, and ...
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OOAD Object Oriented Analysis & Design
When is the need to do OOAD (Object oriented analysis & design)
subang
- Jan 17th, 2010
We use OOAD in order to design the system when overall requirements needed for developing the new system are clearly understood by the developer.
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Data Mart Data
Is data, transient or periodic in data mart? Explain
ashishsinha.dummy
- Nov 10th, 2009
Data marts are part of OLAP system. Data in datamarts cannot be trasient. Data will be updated in datamarts on a periodic or daily basis depending on the design of the data warehouse.-Ashish
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What is the application of data mining tools?
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SCD types and usages
What are the SCD types and what are its Usage?
pradeep16
- Oct 21st, 2009
SCD - Slowly Changing Dimensions. SCD usages in data warehouse are to store the history of transactions on dimensions. There are three types SCDs: Type1 - Just rewrite the record if there is a change...
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If you source data from a system that you (warehouse team) have no authority to enforce data quality at the source level, how do you handle this? Do you apply data validation just after you stage the data? Or do you enforce it at the “end” of the flow (typically as you’re pushing it into the data mart)? Or do you not enforce validation at all?
Dominique
- Sep 10th, 2009
A DW is no longer relevant when it contains inconsistent data. The strength of a DW - and its robustness - also relies on the validation processes... The sooner you can detect and report any inconsist...
Nitin
- Mar 17th, 2007
One of the main purpose of Datawarehousing is to get Cleanse data.So skipping data validatons is not a good idea.It will be better to bring the data to a staging area and perform validations.Or while pushing data from the source we can perform data validations.
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